Individual differences in attention influence perceptual decision making

• Main Authors: Michael Dawson Nunez, Ramesh eSrinivasan, Joachim eVandekerckhove
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2015-02-01
• Series: Frontiers in Psychology
• Subjects: individual differences; phase-locking; Electroencephalography (EEG); perceptual decision making; diffusion models; steady-state visual evoked potential (SSVEP)
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.00018/full

Sequential sampling decision-making models have been successful in accounting for reactiontime (RT) and accuracy data in two-alternative forced choice tasks. These models have beenused to describe the behavior of populations of participants, and explanatory structures havebeen proposed to account for between individual variability in model parameters. In this studywe show that individual differences in behavior from a novel perceptual decision making taskcan be attributed to 1) differences in evidence accumulation rates, 2) differences in variability ofevidence accumulation within trials, and 3) differences in non-decision times across individuals.Using electroencephalography (EEG), we demonstrate that these differences in cognitivevariables, in turn, can be explained by attentional differences as measured by phase-lockingof steady-state visual evoked potential (SSVEP) responses to the signal and noise componentsof the visual stimulus. Parameters of a cognitive model (a diffusion model) were obtained fromaccuracy and RT distributions and related to phase-locking indices (PLIs) of SSVEPs with asingle step in a hierarchical Bayesian framework. Participants who were able to suppress theSSVEP response to visual noise in high frequency bands were able to accumulate correctevidence faster and had shorter non-decision times (preprocessing or motor response times),leading to more accurate responses and faster response times. We show that the combinationof cognitive modeling and neural data in a hierarchical Bayesian framework relates physiologicalprocesses to the cognitive processes of participants, and that a model with a new (out-of-sample) participant’s neural data can predict that participant’s behavior more accurately thanmodels without physiological data.